无法重新分区DStream

Question

val sparkConf = new SparkConf().setMaster("yarn-cluster")
                               .setAppName("SparkJob")
                               .set("spark.executor.memory","2G")
                               .set("spark.dynamicAllocation.executorIdleTimeout","5")

val streamingContext = new StreamingContext(sparkConf, Minutes(1))

var historyRdd: RDD[(String, ArrayList[String])] = streamingContext.sparkContext.emptyRDD

var historyRdd_2: RDD[(String, ArrayList[String])] = streamingContext.sparkContext.emptyRDD

val stream_1 = KafkaUtils.createDirectStream[String, GenericData.Record, StringDecoder, GenericDataRecordDecoder](streamingContext, kafkaParams ,  Set(inputTopic_1))
val dstream_2 = KafkaUtils.createDirectStream[String, GenericData.Record, StringDecoder, GenericDataRecordDecoder](streamingContext, kafkaParams ,  Set(inputTopic_2))

val dstream_2 = stream_2.map((r: Tuple2[String, GenericData.Record]) => 
{
    //some mapping
}

dstream_1.foreachRDD(r => r.repartition(500))
val historyDStream = dstream_1.transform(rdd => rdd.union(historyRdd))
dstream_2.foreachRDD(r => r.repartition(500))
val historyDStream_2 = dstream_2.transform(rdd => rdd.union(historyRdd_2))
val fullJoinResult = historyDStream.fullOuterJoin(historyDStream_2)

val filtered = fullJoinResult.filter(r => r._2._1.isEmpty)


filtered.foreachRDD{rdd =>
    val formatted = rdd.map(r  => (r._1 , r._2._2.get)) 
    historyRdd_2.unpersist(false) // unpersist the 'old' history RDD
    historyRdd_2 = formatted // assign the new history
    historyRdd_2.persist(StorageLevel.MEMORY_AND_DISK) // cache the computation
}

val filteredStream = fullJoinResult.filter(r => r._2._2.isEmpty)

filteredStream.foreachRDD{rdd =>
    val formatted = rdd.map(r => (r._1 , r._2._1.get)) 
    historyRdd.unpersist(false) // unpersist the 'old' history RDD
    historyRdd = formatted // assign the new history
    historyRdd.persist(StorageLevel.MEMORY_AND_DISK) // cache the computation
}

streamingContext.start()
streamingContext.awaitTermination()
}
}

我无法使用上面的代码重新划分DStream，我得到了128个分区作为输入，这就是no。对于Kafka分区，由于Join，我需要对读写数据进行洗牌，所以我想通过增加无分区来增加并行性。但是分区仍然是same.Why，是吗？

​

Answer 1

就像map或filter一样，repartition是星火中的一种转变，意味着三件事：

• 它返回另一个不可变的RDD。
• 太懒了
• 它需要通过一些行动来实现

考虑到这一守则：

dstream_1.foreachRDD(r => r.repartition(500))

在repartition中使用foreachRDD作为副作用什么也不做.生成的RDD从未被使用过，因此重新分区永远不会发生。

我们应该把这一转变与工作中的其他行动联系起来。在这种情况下，实现这一目标的一个简单方法是使用transform：

val repartitionedDStream = dstream_1.transform(rdd => rdd.repartition(500))
... use repartitionedDStream further on ...